Combining chemotherapeutics to treat malignant tumors has been shown to be effectivein preventing drug resistance, tumor recurrence, and reducing tumor size. We modeledcombination drug therapy in PC-3 human prostate cancer cells using mixture design responsesurface methodology (MDRSM), a statistical technique designed to optimize compositions thatwe applied in a novel manner to design combinations of chemotherapeutics. Conventionalchemotherapeutics (mitoxantrone, cabazitaxel, and docetaxel) and natural bioactive compounds(resveratrol, piperlongumine, and flavopiridol) were used in twelve different combinationscontaining three drugs at varying concentrations. Cell viability and cell cycle data werecollected and used to plot response surfaces in MDRSM that identified the most effectiveconcentrations of each drug in combination. MDRSM allows for extrapolation of data fromthree or more compounds in variable ratio combinations, unlike the Chou-Talalay method.MDRSM combinations were compared with combination index data from the Chou-Talalaymethod and were found to coincide. We propose MDRSM as an effective tool in devisingcombination treatments that can improve treatment effectiveness, and increase treatmentpersonalization because MDRSM measures effectiveness rather than synergism, potentiation orantagonism.